R/AllGenerics.R
In YuLab-SMU/enrichplot: Visualization of Functional Enrichment Result
##' dotplot for enrichment result
##'
##'
##' @title dotplot
##' @rdname dotplot
##' @param object input object
##' @param ... additional parameters
##' @return plot
##' @importFrom methods setGeneric
##' @export
##' @examples
##' \dontrun{
##' library(DOSE)
##' data(geneList)
##' de <- names(geneList)[1:100]
##' x <- enrichDO(de)
##' dotplot(x)
##' # use `showCategory` to select the displayed terms. It can be a number of a vector of terms.
##' dotplot(x, showCategory = 10)
##' categorys <- c("pre-malignant neoplasm", "intestinal disease",
##' "breast ductal carcinoma", "non-small cell lung carcinoma")
##' dotplot(x, showCategory = categorys)
##' # It can also graph compareClusterResult
##' data(gcSample)
##' library(clusterProfiler)
##' library(DOSE)
##' library(org.Hs.eg.db)
##' data(gcSample)
##' xx <- compareCluster(gcSample, fun="enrichGO", OrgDb="org.Hs.eg.db")
##' xx2 <- pairwise_termsim(xx)
##' library(ggstar)
##' dotplot(xx2)
##' dotplot(xx2, shape = TRUE)
##' dotplot(xx2, group = TRUE)
##' dotplot(xx2, x = "GeneRatio", group = TRUE, size = "count")
##' }
##' @author Guangchuang Yu
setGeneric("dotplot",
function(object, ...)
standardGeneric("dotplot")
)
##' Enrichment Map for enrichment result of
##' over-representation test or gene set enrichment analysis
##'
##'
##' This function visualizes gene sets as a network (i.e. enrichment map).
##' Mutually overlapping gene sets tend to cluster together, making it
##' easier for interpretation. When the similarity between terms meets
##' a certain threshold (default is 0.2, adjusted by parameter `min_edge`),
##' there will be edges between terms. The stronger the similarity,
##' the shorter and thicker the edges. The similarity between terms is
##' obtained by function `pairwise_termsim`, the details of similarity
##' calculation can be found in its documentation: \link{pairwise_termsim}.
##' @title emapplot
##' @rdname emapplot
##' @param x Enrichment result.
##' @param showCategory A number or a vector of terms. If it is a number,
##' the first n terms will be displayed. If it is a vector of terms,
##' the selected terms will be displayed.
##' @param ... Additional parameters
##' @return ggplot object
##' @export
##' @examples
##' \dontrun{
##' library(DOSE)
##' data(geneList)
##' de <- names(geneList)[1:100]
##' x <- enrichDO(de)
##' x2 <- pairwise_termsim(x)
##' emapplot(x2)
##' # use `layout` to change the layout of map
##' emapplot(x2, layout = "star")
##' # use `showCategory` to select the displayed terms. It can be a number of a vector of terms.
##' emapplot(x2, showCategory = 10)
##' categorys <- c("pre-malignant neoplasm", "intestinal disease",
##' "breast ductal carcinoma")
##' emapplot(x2, showCategory = categorys)
##'
##' # It can also graph compareClusterResult
##' library(clusterProfiler)
##' library(DOSE)
##' library(org.Hs.eg.db)
##' data(gcSample)
##' xx <- compareCluster(gcSample, fun="enrichGO", OrgDb="org.Hs.eg.db")
##' xx2 <- pairwise_termsim(xx)
##' emapplot(xx2)
##' }
##' @author Guangchuang Yu
setGeneric("emapplot",
function(x, ...)
standardGeneric("emapplot")
)
##' Get the similarity matrix
##'
##'
##' This function add similarity matrix to the termsim slot of enrichment result.
##' Users can use the `method` parameter to select the method of calculating similarity.
##' The Jaccard correlation coefficient(JC) is used by default, and it applies to all situations.
##' When users want to calculate the correlation between GO terms or DO terms, they can also choose
##' "Resnik", "Lin", "Rel" or "Jiang" (they are semantic similarity calculation methods from GOSemSim packages),
##' and at this time, the user needs to provide `semData` parameter, which can be obtained through
##' \link{godata} function in GOSemSim package.
##' @title pairwise_termsim
##' @rdname pairwise_termsim
##' @param x enrichment result.
##' @param method method of calculating the similarity between nodes,
##' one of "Resnik", "Lin", "Rel", "Jiang" , "Wang" and
##' "JC"(Jaccard similarity coefficient) methods.
##' @param semData GOSemSimDATA object, can be obtained through
##' \link{godata} function in GOSemSim package.
##' @param showCategory number of enriched terms to display, default value is 200.
##' @examples
##' \dontrun{
##' library(clusterProfiler)
##' library(org.Hs.eg.db)
##' library(enrichplot)
##' library(GOSemSim)
##' library(DOSE)
##' data(geneList)
##' gene <- names(geneList)[abs(geneList) > 2]
##' ego <- enrichGO(gene = gene,
##' universe = names(geneList),
##' OrgDb = org.Hs.eg.db,
##' ont = "BP",
##' pAdjustMethod = "BH",
##' pvalueCutoff = 0.01,
##' qvalueCutoff = 0.05,
##' readable = TRUE)
##' d <- godata('org.Hs.eg.db', ont="BP")
##' ego2 <- pairwise_termsim(ego, method="Wang", semData = d)
##' emapplot(ego2)
##' emapplot_cluster(ego2)
##' }
setGeneric("pairwise_termsim",
function(x, method = "JC", semData = NULL, showCategory = 200)
standardGeneric("pairwise_termsim")
)
##' plot induced GO DAG of significant terms
##'
##'
##' @title goplot
##' @rdname goplot
##' @param x enrichment result.
##' @param showCategory number of enriched terms to display
##' @param color variable that used to color enriched terms, e.g. pvalue,
##' p.adjust or qvalue
##' @param layout layout of the map
##' @param geom label geom, one of 'label' or 'text'
##' @param ... additional parameter
##' @return ggplot object
##' @export
##' @examples
##' \dontrun{
##' library(clusterProfiler)
##' data(geneList, package = "DOSE")
##' de <- names(geneList)[1:100]
##' yy <- enrichGO(de, 'org.Hs.eg.db', ont="BP", pvalueCutoff=0.01)
##' goplot(yy)
##' goplot(yy, showCategory = 5)
##' }
##' @author Guangchuang Yu
setGeneric("goplot",
function(x, showCategory = 10, color = "p.adjust",
layout = "sugiyama", geom = "text", ...)
standardGeneric("goplot")
)
##' visualize analyzing result of GSEA
##'
##' plotting function for gseaResult
##' @title gseaplot
##' @rdname gseaplot
##' @param x object of gsea result
##' @param geneSetID geneSet ID
##' @param by one of "runningScore" or "position"
##' @param title plot title
##' @param ... additional parameters
##' @return ggplot2 object
##' @export
##' @examples
##' library(DOSE)
##' data(geneList)
##' x <- gseDO(geneList)
##' gseaplot(x, geneSetID=1)
##' @author Guangchuang Yu
setGeneric("gseaplot",
function(x, geneSetID, by = "all", title = "", ...) {
standardGeneric("gseaplot")
})
##' heatmap like plot for functional classification
##'
##'
##' @title heatplot
##' @rdname heatplot
##' @param x enrichment result.
##' @param showCategory number of enriched terms to display
##' @param foldChange fold Change.
##' @param label_format a numeric value sets wrap length, alternatively a
##' custom function to format axis labels.
##' @param ... Additional parameters
##' @export
##' @return ggplot object
##' @examples
##' library(DOSE)
##' data(geneList)
##' de <- names(geneList)[1:100]
##' x <- enrichDO(de)
##' heatplot(x)
##' @author Guangchuang Yu
setGeneric("heatplot",
function(x, showCategory = 30, ...)
standardGeneric("heatplot")
)
##' volcano plot for enrichment result
##'
##'
##' @title volplot
##' @rdname volplot
##' @param x enrichment result.
##' @param color selected variable to color the dots
##' @param xintercept value to set x intercept
##' @param yintercept value to set y intercept
##' @param showCategory number of most significant enriched terms or selected terms to
##' display determined by the variable selected to color the dots
##' @param label_format a numeric value sets wrap length, alternatively a
##' custom function to format axis labels.
##' @param ... Additional parameters
##' @export
##' @return ggplot object
##' @examples
##' library(DOSE)
##' data(geneList)
##' de <- names(geneList)[1:100]
##' x <- enrichDO(de)
##' volplot(x)
##' @author Guangchuang Yu
setGeneric("volplot",
function(x, color = "zScore",
xintercept = 1, yintercept = 2,
showCategory = 5, label_format = 30,
...)
standardGeneric("volplot")
)
##' ridgeline plot for GSEA result
##'
##'
##' @title ridgeplot
##' @rdname ridgeplot
##' @param x gseaResult object
##' @param showCategory A number or a vector of terms. If it is a number,
##' the first n terms will be displayed. If it is a vector of terms,
##' the selected terms will be displayed.
##' @param fill one of "pvalue", "p.adjust", "qvalue"
##' @param core_enrichment whether only using core_enriched genes
##' @param label_format a numeric value sets wrap length, alternatively a
##' custom function to format axis labels.
##' @param ... additional parameters
##' by default wraps names longer that 30 characters
##' @return ggplot object
##' @export
##' @examples
##' library(DOSE)
##' data(geneList)
##' x <- gseDO(geneList)
##' ridgeplot(x)
##' @author Guangchuang Yu
setGeneric("ridgeplot",
function(x, showCategory=30, fill="p.adjust", core_enrichment = TRUE,
label_format = 30, ...)
standardGeneric("ridgeplot")
)
##' upsetplot method generics
##'
##'
##' @docType methods
##' @name upsetplot
##' @rdname upsetplot-methods
##' @title upsetplot method
##' @param x object
##' @param ... additional parameters
##' @return plot
##' @export
##' @author Guangchuang Yu
setGeneric("upsetplot", function(x, ...) standardGeneric("upsetplot"))
##' Functional grouping tree diagram for enrichment result of
##' over-representation test or gene set enrichment analysis.
##'
##'
##' This function visualizes gene sets as a tree.
##' Gene sets with high similarity tend to cluster together, making it easier
##' for interpretation.
##' @title treeplot
##' @rdname treeplot
##' @param x enrichment result.
##' @param showCategory number of enriched terms to display
##' @param color variable that used to color enriched terms, e.g. pvalue,
##' p.adjust or qvalue
##' @param label_format a numeric value sets wrap length, alternatively a
##' custom function to format axis labels.
##' @param ... additional parameters
##' @return ggplot object
##' @export
##' @examples
##' \dontrun{
##' library(clusterProfiler)
##' library(org.Hs.eg.db)
##' library(enrichplot)
##' library(GOSemSim)
##' library(ggplot2)
##' library(DOSE)
##' data(geneList)
##' gene <- names(geneList)[abs(geneList) > 2]
##' ego <- enrichGO(gene = gene,
##' universe = names(geneList),
##' OrgDb = org.Hs.eg.db,
##' ont = "BP",
##' pAdjustMethod = "BH",
##' pvalueCutoff = 0.01,
##' qvalueCutoff = 0.05,
##' readable = TRUE)
##' d <- godata('org.Hs.eg.db', ont="BP")
##' ego2 <- pairwise_termsim(ego, method = "Wang", semData = d)
##' treeplot(ego2, showCategory = 30)
##' # use `hilight = FALSE` to remove ggtree::geom_hilight() layer.
##' treeplot(ego2, showCategory = 30, hilight = FALSE)
##' # use `offset` parameter to adjust the distance of bar and tree.
##' treeplot(ego2, showCategory = 30, hilight = FALSE, offset = rel(1.5))
##' # use `offset_tiplab` parameter to adjust the distance of nodes and branches.
##' treeplot(ego2, showCategory = 30, hilight = FALSE, offset_tiplab = rel(1.5))
##' keep <- rownames(ego2@termsim)[c(1:10, 16:20)]
##' keep
##' treeplot(ego2, showCategory = keep)
##' treeplot(ego2, showCategory = 20,
##' group_color = c("#999999", "#E69F00", "#56B4E9", "#009E73", "#F0E442"))
##' # It can also graph compareClusterResult
##' data(gcSample)
##' xx <- compareCluster(gcSample, fun="enrichKEGG",
##' organism="hsa", pvalueCutoff=0.05)
##' xx <- pairwise_termsim(xx)
##' treeplot(xx)
##'
##' # use `geneClusterPanel` to change the gene cluster panel.
##' treeplot(xx, geneClusterPanel = "dotplot")
##'
##' treeplot(xx, geneClusterPanel = "pie")
##' }
setGeneric("treeplot",
function(x, ...)
standardGeneric("treeplot")
)
##' Similarity space plot of enrichment analysis results.
##'
##' @title ssplot
##' @rdname ssplot
##' @inheritParams emapplot
##' @return ggplot object
##' @export
##' @examples
##' \dontrun{
##' library(clusterProfiler)
##' library(org.Hs.eg.db)
##' library(enrichplot)
##' library(GOSemSim)
##' library(DOSE)
##' data(geneList)
##' gene <- names(geneList)[abs(geneList) > 2]
##' ego <- enrichGO(gene = gene,
##' universe = names(geneList),
##' OrgDb = org.Hs.eg.db,
##' ont = "BP",
##' pAdjustMethod = "BH",
##' pvalueCutoff = 0.01,
##' qvalueCutoff = 0.05,
##' readable = TRUE)
##' d <- godata('org.Hs.eg.db', ont="BP")
##' ego2 <- pairwise_termsim(ego, method = "Wang", semData = d)
##' ssplot(ego2)
##' }
##' @author Guangchuang Yu
setGeneric("ssplot",
function(x, ...)
standardGeneric("ssplot")
)
YuLab-SMU/enrichplot documentation built on Jan. 10, 2025, 4:44 a.m.
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##' dotplot for enrichment result
##'
##'
##' @title dotplot
##' @rdname dotplot
##' @param object input object
##' @param ... additional parameters
##' @return plot
##' @importFrom methods setGeneric
##' @export
##' @examples
##' \dontrun{
##' library(DOSE)
##' data(geneList)
##' de <- names(geneList)[1:100]
##' x <- enrichDO(de)
##' dotplot(x)
##' # use `showCategory` to select the displayed terms. It can be a number of a vector of terms.
##' dotplot(x, showCategory = 10)
##' categorys <- c("pre-malignant neoplasm", "intestinal disease",
##' "breast ductal carcinoma", "non-small cell lung carcinoma")
##' dotplot(x, showCategory = categorys)
##' # It can also graph compareClusterResult
##' data(gcSample)
##' library(clusterProfiler)
##' library(DOSE)
##' library(org.Hs.eg.db)
##' data(gcSample)
##' xx <- compareCluster(gcSample, fun="enrichGO", OrgDb="org.Hs.eg.db")
##' xx2 <- pairwise_termsim(xx)
##' library(ggstar)
##' dotplot(xx2)
##' dotplot(xx2, shape = TRUE)
##' dotplot(xx2, group = TRUE)
##' dotplot(xx2, x = "GeneRatio", group = TRUE, size = "count")
##' }
##' @author Guangchuang Yu
setGeneric("dotplot",
function(object, ...)
standardGeneric("dotplot")
)
##' Enrichment Map for enrichment result of
##' over-representation test or gene set enrichment analysis
##'
##'
##' This function visualizes gene sets as a network (i.e. enrichment map).
##' Mutually overlapping gene sets tend to cluster together, making it
##' easier for interpretation. When the similarity between terms meets
##' a certain threshold (default is 0.2, adjusted by parameter `min_edge`),
##' there will be edges between terms. The stronger the similarity,
##' the shorter and thicker the edges. The similarity between terms is
##' obtained by function `pairwise_termsim`, the details of similarity
##' calculation can be found in its documentation: \link{pairwise_termsim}.
##' @title emapplot
##' @rdname emapplot
##' @param x Enrichment result.
##' @param showCategory A number or a vector of terms. If it is a number,
##' the first n terms will be displayed. If it is a vector of terms,
##' the selected terms will be displayed.
##' @param ... Additional parameters
##' @return ggplot object
##' @export
##' @examples
##' \dontrun{
##' library(DOSE)
##' data(geneList)
##' de <- names(geneList)[1:100]
##' x <- enrichDO(de)
##' x2 <- pairwise_termsim(x)
##' emapplot(x2)
##' # use `layout` to change the layout of map
##' emapplot(x2, layout = "star")
##' # use `showCategory` to select the displayed terms. It can be a number of a vector of terms.
##' emapplot(x2, showCategory = 10)
##' categorys <- c("pre-malignant neoplasm", "intestinal disease",
##' "breast ductal carcinoma")
##' emapplot(x2, showCategory = categorys)
##'
##' # It can also graph compareClusterResult
##' library(clusterProfiler)
##' library(DOSE)
##' library(org.Hs.eg.db)
##' data(gcSample)
##' xx <- compareCluster(gcSample, fun="enrichGO", OrgDb="org.Hs.eg.db")
##' xx2 <- pairwise_termsim(xx)
##' emapplot(xx2)
##' }
##' @author Guangchuang Yu
setGeneric("emapplot",
function(x, ...)
standardGeneric("emapplot")
)
##' Get the similarity matrix
##'
##'
##' This function add similarity matrix to the termsim slot of enrichment result.
##' Users can use the `method` parameter to select the method of calculating similarity.
##' The Jaccard correlation coefficient(JC) is used by default, and it applies to all situations.
##' When users want to calculate the correlation between GO terms or DO terms, they can also choose
##' "Resnik", "Lin", "Rel" or "Jiang" (they are semantic similarity calculation methods from GOSemSim packages),
##' and at this time, the user needs to provide `semData` parameter, which can be obtained through
##' \link{godata} function in GOSemSim package.
##' @title pairwise_termsim
##' @rdname pairwise_termsim
##' @param x enrichment result.
##' @param method method of calculating the similarity between nodes,
##' one of "Resnik", "Lin", "Rel", "Jiang" , "Wang" and
##' "JC"(Jaccard similarity coefficient) methods.
##' @param semData GOSemSimDATA object, can be obtained through
##' \link{godata} function in GOSemSim package.
##' @param showCategory number of enriched terms to display, default value is 200.
##' @examples
##' \dontrun{
##' library(clusterProfiler)
##' library(org.Hs.eg.db)
##' library(enrichplot)
##' library(GOSemSim)
##' library(DOSE)
##' data(geneList)
##' gene <- names(geneList)[abs(geneList) > 2]
##' ego <- enrichGO(gene = gene,
##' universe = names(geneList),
##' OrgDb = org.Hs.eg.db,
##' ont = "BP",
##' pAdjustMethod = "BH",
##' pvalueCutoff = 0.01,
##' qvalueCutoff = 0.05,
##' readable = TRUE)
##' d <- godata('org.Hs.eg.db', ont="BP")
##' ego2 <- pairwise_termsim(ego, method="Wang", semData = d)
##' emapplot(ego2)
##' emapplot_cluster(ego2)
##' }
setGeneric("pairwise_termsim",
function(x, method = "JC", semData = NULL, showCategory = 200)
standardGeneric("pairwise_termsim")
)
##' plot induced GO DAG of significant terms
##'
##'
##' @title goplot
##' @rdname goplot
##' @param x enrichment result.
##' @param showCategory number of enriched terms to display
##' @param color variable that used to color enriched terms, e.g. pvalue,
##' p.adjust or qvalue
##' @param layout layout of the map
##' @param geom label geom, one of 'label' or 'text'
##' @param ... additional parameter
##' @return ggplot object
##' @export
##' @examples
##' \dontrun{
##' library(clusterProfiler)
##' data(geneList, package = "DOSE")
##' de <- names(geneList)[1:100]
##' yy <- enrichGO(de, 'org.Hs.eg.db', ont="BP", pvalueCutoff=0.01)
##' goplot(yy)
##' goplot(yy, showCategory = 5)
##' }
##' @author Guangchuang Yu
setGeneric("goplot",
function(x, showCategory = 10, color = "p.adjust",
layout = "sugiyama", geom = "text", ...)
standardGeneric("goplot")
)
##' visualize analyzing result of GSEA
##'
##' plotting function for gseaResult
##' @title gseaplot
##' @rdname gseaplot
##' @param x object of gsea result
##' @param geneSetID geneSet ID
##' @param by one of "runningScore" or "position"
##' @param title plot title
##' @param ... additional parameters
##' @return ggplot2 object
##' @export
##' @examples
##' library(DOSE)
##' data(geneList)
##' x <- gseDO(geneList)
##' gseaplot(x, geneSetID=1)
##' @author Guangchuang Yu
setGeneric("gseaplot",
function(x, geneSetID, by = "all", title = "", ...) {
standardGeneric("gseaplot")
})
##' heatmap like plot for functional classification
##'
##'
##' @title heatplot
##' @rdname heatplot
##' @param x enrichment result.
##' @param showCategory number of enriched terms to display
##' @param foldChange fold Change.
##' @param label_format a numeric value sets wrap length, alternatively a
##' custom function to format axis labels.
##' @param ... Additional parameters
##' @export
##' @return ggplot object
##' @examples
##' library(DOSE)
##' data(geneList)
##' de <- names(geneList)[1:100]
##' x <- enrichDO(de)
##' heatplot(x)
##' @author Guangchuang Yu
setGeneric("heatplot",
function(x, showCategory = 30, ...)
standardGeneric("heatplot")
)
##' volcano plot for enrichment result
##'
##'
##' @title volplot
##' @rdname volplot
##' @param x enrichment result.
##' @param color selected variable to color the dots
##' @param xintercept value to set x intercept
##' @param yintercept value to set y intercept
##' @param showCategory number of most significant enriched terms or selected terms to
##' display determined by the variable selected to color the dots
##' @param label_format a numeric value sets wrap length, alternatively a
##' custom function to format axis labels.
##' @param ... Additional parameters
##' @export
##' @return ggplot object
##' @examples
##' library(DOSE)
##' data(geneList)
##' de <- names(geneList)[1:100]
##' x <- enrichDO(de)
##' volplot(x)
##' @author Guangchuang Yu
setGeneric("volplot",
function(x, color = "zScore",
xintercept = 1, yintercept = 2,
showCategory = 5, label_format = 30,
...)
standardGeneric("volplot")
)
##' ridgeline plot for GSEA result
##'
##'
##' @title ridgeplot
##' @rdname ridgeplot
##' @param x gseaResult object
##' @param showCategory A number or a vector of terms. If it is a number,
##' the first n terms will be displayed. If it is a vector of terms,
##' the selected terms will be displayed.
##' @param fill one of "pvalue", "p.adjust", "qvalue"
##' @param core_enrichment whether only using core_enriched genes
##' @param label_format a numeric value sets wrap length, alternatively a
##' custom function to format axis labels.
##' @param ... additional parameters
##' by default wraps names longer that 30 characters
##' @return ggplot object
##' @export
##' @examples
##' library(DOSE)
##' data(geneList)
##' x <- gseDO(geneList)
##' ridgeplot(x)
##' @author Guangchuang Yu
setGeneric("ridgeplot",
function(x, showCategory=30, fill="p.adjust", core_enrichment = TRUE,
label_format = 30, ...)
standardGeneric("ridgeplot")
)
##' upsetplot method generics
##'
##'
##' @docType methods
##' @name upsetplot
##' @rdname upsetplot-methods
##' @title upsetplot method
##' @param x object
##' @param ... additional parameters
##' @return plot
##' @export
##' @author Guangchuang Yu
setGeneric("upsetplot", function(x, ...) standardGeneric("upsetplot"))
##' Functional grouping tree diagram for enrichment result of
##' over-representation test or gene set enrichment analysis.
##'
##'
##' This function visualizes gene sets as a tree.
##' Gene sets with high similarity tend to cluster together, making it easier
##' for interpretation.
##' @title treeplot
##' @rdname treeplot
##' @param x enrichment result.
##' @param showCategory number of enriched terms to display
##' @param color variable that used to color enriched terms, e.g. pvalue,
##' p.adjust or qvalue
##' @param label_format a numeric value sets wrap length, alternatively a
##' custom function to format axis labels.
##' @param ... additional parameters
##' @return ggplot object
##' @export
##' @examples
##' \dontrun{
##' library(clusterProfiler)
##' library(org.Hs.eg.db)
##' library(enrichplot)
##' library(GOSemSim)
##' library(ggplot2)
##' library(DOSE)
##' data(geneList)
##' gene <- names(geneList)[abs(geneList) > 2]
##' ego <- enrichGO(gene = gene,
##' universe = names(geneList),
##' OrgDb = org.Hs.eg.db,
##' ont = "BP",
##' pAdjustMethod = "BH",
##' pvalueCutoff = 0.01,
##' qvalueCutoff = 0.05,
##' readable = TRUE)
##' d <- godata('org.Hs.eg.db', ont="BP")
##' ego2 <- pairwise_termsim(ego, method = "Wang", semData = d)
##' treeplot(ego2, showCategory = 30)
##' # use `hilight = FALSE` to remove ggtree::geom_hilight() layer.
##' treeplot(ego2, showCategory = 30, hilight = FALSE)
##' # use `offset` parameter to adjust the distance of bar and tree.
##' treeplot(ego2, showCategory = 30, hilight = FALSE, offset = rel(1.5))
##' # use `offset_tiplab` parameter to adjust the distance of nodes and branches.
##' treeplot(ego2, showCategory = 30, hilight = FALSE, offset_tiplab = rel(1.5))
##' keep <- rownames(ego2@termsim)[c(1:10, 16:20)]
##' keep
##' treeplot(ego2, showCategory = keep)
##' treeplot(ego2, showCategory = 20,
##' group_color = c("#999999", "#E69F00", "#56B4E9", "#009E73", "#F0E442"))
##' # It can also graph compareClusterResult
##' data(gcSample)
##' xx <- compareCluster(gcSample, fun="enrichKEGG",
##' organism="hsa", pvalueCutoff=0.05)
##' xx <- pairwise_termsim(xx)
##' treeplot(xx)
##'
##' # use `geneClusterPanel` to change the gene cluster panel.
##' treeplot(xx, geneClusterPanel = "dotplot")
##'
##' treeplot(xx, geneClusterPanel = "pie")
##' }
setGeneric("treeplot",
function(x, ...)
standardGeneric("treeplot")
)
##' Similarity space plot of enrichment analysis results.
##'
##' @title ssplot
##' @rdname ssplot
##' @inheritParams emapplot
##' @return ggplot object
##' @export
##' @examples
##' \dontrun{
##' library(clusterProfiler)
##' library(org.Hs.eg.db)
##' library(enrichplot)
##' library(GOSemSim)
##' library(DOSE)
##' data(geneList)
##' gene <- names(geneList)[abs(geneList) > 2]
##' ego <- enrichGO(gene = gene,
##' universe = names(geneList),
##' OrgDb = org.Hs.eg.db,
##' ont = "BP",
##' pAdjustMethod = "BH",
##' pvalueCutoff = 0.01,
##' qvalueCutoff = 0.05,
##' readable = TRUE)
##' d <- godata('org.Hs.eg.db', ont="BP")
##' ego2 <- pairwise_termsim(ego, method = "Wang", semData = d)
##' ssplot(ego2)
##' }
##' @author Guangchuang Yu
setGeneric("ssplot",
function(x, ...)
standardGeneric("ssplot")
)
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